用张量对齐方法提升跨域任务的准确率与速度
An Unsupervised Tensor-Based Domain Alignment
- 在不变子空间中通过迭代优化对齐矩阵实现源域与目标域张量对齐
- 相比传统方法，分类准确率显著提升，且转换速度更快
- 适合复杂跨域场景，尤其对张量结构数据有优势
我们提出一种基于张量的域自适应（DA）算法，通过在不变子空间内使用对齐矩阵来对齐源域和目标域张量。该对齐矩阵与子空间通过迭代优化，其中约束位于斜流形（oblique manifold），相较于传统的施蒂费尔流形（Stiefel manifold）更具灵活性和适应性。此外，通过定义正则化项以保持源域和目标域张量的方差，确保了模型的鲁棒性。我们的框架具有通用性，能涵盖现有张量基域自适应方法作为特例。大量实验表明，该方法不仅提升了域自适应转换速度，还显著提高了分类精度，优于当前最先进的技术，是复杂域自适应任务的理想选择。
原文摘要 · Abstract (English)
We propose a tensor-based domain alignment (DA) algorithm designed to align source and target tensors within an invariant subspace through the use of alignment matrices. These matrices along with the subspace undergo iterative optimization of which constraint is on oblique manifold, which offers greater flexibility and adaptability compared to the traditional Stiefel manifold. Moreover, regularization terms defined to preserve the variance of both source and target tensors, ensures robust performance. Our framework is versatile, effectively generalizing existing tensor-based DA methods as special cases. Through extensive experiments, we demonstrate that our approach not only enhances DA conversion speed but also significantly boosts classification accuracy. This positions our method as superior to current state-of-the-art techniques, making it a preferable choice for complex domain adaptation tasks.
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